University of Groningen

The biomechanical outcome after total hip replacementWeber, Tim

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CHAPTER 4

Gait six month and one-year after computer assisted Femur First

THR vs. conventional THR. Results of a patient- and observer-

blinded randomized controlled trial

submitted as:

T. Weber, S. Dendorfer, SK.Bulstra, J. Grifka, GJ. Verkerke and T. Renkawitz– “Gait six month and one-year after computer assisted Femur First

THR vs. conventional THR. Results of a patient- and observer- blinded randomized controlled trial” - Gait and Posture

Abstract

BACKGROUND: A correct combined orientation of the Total Hip Replacement (THR) im-

plant components leads to a maximized, stable range of motion (ROM) while decreasing the

risk of dislocation. A computer-assisted surgical method (CAS) for THR following the concept

of ’femur first/combined anteversion’ (CAS FF) to improve the combined implant positioning

has been developed. A patient- and observer- blinded, randomized study is presented that is

used to assess whether patient’s postoperative gait parameters is improved by CAS FF oper-

ation method for THR in comparison to conventional THR (CON) six months and one year

after surgery.

METHODS: 60 patients were enrolled randomized for conventional, freehand THR (n = 32)

or the navigated THR (n = 28). All patients underwent a 3D motion-capture gait analysis

of the lower extremity at pre-operative (t0), 6 months post-operative (t1) and 12 months

post-operative (t2).

RESULTS: We realized a trend for an improved hip flexion angle for the CAS FF group.

Dimensionless walking speed as well as range of motion and symmetry thereof did show a clear

improvement over the follow-up period, but no statistical differences between the intervention

groups were found.

DISCUSSION: While all parameters do increase over the follow-up period for the CAS FF

and CON group, there were no significant differences between the groups six month and one

year after surgery. Patients undergoing CAS FF showed a trend to improved hip flexion angle

compared to CON patients, with a possible long-term benefit of reducing wear in the implant

system, which remains to be proven.

51

Chapter IV Gait FF Patients and Methods

Introduction

One of the major problems during Total Hip Replacement (THR) is to find an optimized com-

promise between the trias of hip biomechanics, tribology and postoperative functionality. In the

end, all three elements are dependent on each other: The position of total hip components cor-

relates to the risk for dislocation, implant failure, articular wear and prosthetic range of motion

(ROM). Early impingement occurs when contact between the prosthetic femoral neck, the acetab-

ular cup and/or bony parts (e.g. greater trochanter, acetabular rim, resection plane) occurs within

a patients normal range of motion. Several authors have proposed starting with the preparation of

the femur and then transferring the patient-individual orientation of the stem relative to the cup

intraoperatively (’femur first’/’combined anteversion’) in order to minimize the risk of impinge-

ment and dislocation1–3. A novel, computer-assisted surgical method (CAS) for THR following

the concept of ’femur first/combined anteversion’ (CAS FF) has been developed. This incorporates

various aspects of performing a functional optimization of the prosthetic stem and cup position in

order to improve implant component positioning and orientation4–6. While a number of studies

have been conducted in order to determine the objective outcome of conventional THR, no study

so far has investigated the effects of CAS FF on gait7–9.

For quantifying gait several parameters can be used. To quantify the patients ability to restore

walking some researchers measure walking speed as a target parameter, without taking differences

in body height into account10. Since taller persons have a greater leg-length they are also capable

of walking faster. Therefore normalizing the walking speed to leg length is essential, especially for

the comparison of groups11,12. An increasing number of studies also uses joint angles, especially

the hip joint angles (hip flexion hf, hip abduction ha, hip rotation - hr) for THR patients, as

a measure if gait following surgery is pathological or if the biomechanics and therefore function

can be restored8,13,14. Healthy and able-bodied persons walk in a symmetrical way15. Therefore

an important outcome after THR is not only magnitude and orientation of joint angles, but also

symmetry of active range of motion (ROM) as a measure to what extent gait pattern is pathological.

The aim of this patient- and observer- blinded, randomized study was to assess whether patient’s

postoperative gait parameters are improved in comparison between the CAS FF and conventional

THR six months and one year after surgery. Our hypothesis were, (i) CAS FF leads to an improved

dimensionless walking speed compared to conventional THR, (ii) the active range of motion after

CAS FF is improved both in magnitude and in symmetry compared to conventional THR and (iii)

CAS FF leads to an improved hip flexion angle (hf) compared to conventional THR.

Patients and Methods

Patients

During a registered, prospective randomized controlled trial (DRKS00000739, German Clinical Tri-

als Register) we randomized patients for surgery with or without the use of an imageless navigation

52

Patients and Methods Chapter IV Gait FF

system. The random allocation sequence was computer-generated in a permuted block random-

ization design by statisticians of the Institute of Medical Statistics and Epidemiology Munich

using certificated randomization software (Rancode 3.6 Professional, IDV, Gauting, Germany).

Permuted blocks of four, six, and eight participants were used to ensure a balanced allocation

sequence. This sequence then was placed in sealed, consecutively numbered, opaque envelopes.

These envelopes were kept in a locked filing cabinet in the office of the surgeon who opened the en-

velopes in order of participant recruitment on the day of surgery. This investigation was approved

by the local Ethics Commission (No.: 10-121-0263). A sovereign power calculation was performed

for investigation of the three primary endpoints in this subgroup gait analysis: t0 (preop), t1 (6

month post-operative) and t2 (12 month post-operative). Consequently, each of the corresponding

hypotheses was tested on a Bonferroni- adjusted, two-sided (5%/3 = 1.7%) significance level. The

relevant difference between navigation and conventional THR was set at 0.3. Based on these con-

siderations, a sample size of 30 in each group achieved a power of 77% using two-sample t-tests

(nQuery Advisor 7.0, Statistical Solutions Ltd, Cork, Ireland). Patients characteristics according

to allocation are presented in table 1.

Interventiongroup

Samplesize(male/female)

Age inyears:Mean(SD)

Agerange:min/max

BMI(kg/m2):Mean(SD)

BMIrange:min/max

CAS FF 28(10/18) 60(7) 50/74 26.73(4.26) 19.45/35.22CON 32(19/13) 62(8) 50/74 27.58(3.08) 21.64/33.68

Table 1: Patient characteristics by intervention group

Methods

Computer Assisted Femur First THR (CAS FF)

In the navigated group, an imageless navigation system (BrainLAB Prototype Hip 6.0 Femur

First, Feldkirchen, Germany) with newly developed ”Femur First” prototype software was used

(Renkawitz et al. 2012b).

Conventional THR (CON)

Acetabular components were placed freehand without the use of any alignment guides. The target

acetabular component position for all patients was within the ’safe zone’ as defined by Lewinnek

et al. (40◦ ± 10◦ inclination and 15◦ ± 10◦ anteversion)16.

Gait analysis (GA)

All patients performed a 3D motion-capture (MoCap) gait analysis of the lower extremity (SimiMotion R©,

Unterschleissheim, Germany) at three time points (pre-operative (t0), 6 months post-operative (t1)

and 12 months post-operative (t2) Figure1a). A bony and anatomical landmark based marker-set

53

Chapter IV Gait FF Patients and Methods

consisting of 27 retro-reflective markers (Figure1b) was used to record the patient-specific gait

pattern by means of six digital video cameras with a video sample rate of 70Hz9. The patients

walked at self-selected speed on a 10m walkway, while the ground reaction forces were recorded

simultaneously using two force plates (Kistler R©, Winterthur, Schweiz; sample rate:1000Hz). In

order to calculate joint positions based on marker data, a static trial was conducted before the gait

experiment started. Prior to recording, the patients were asked to walk on the walkway three to

five times in order to acquaint themselves with the laboratory situation. One patient missed t1-

gait analysis, but returned for the t2-analysis. GA data was processed with a commercial software

package that is used for musculoskeletal modeling of gait (AnyBody A/S, Aalborg, Denmark). A

generic virtual human body model (AnyGait, AMMR1.6) was first scaled based on anthropometric

measurements as an initial guess17. This was followed by a nonlinear scaling algorithm based on

the maker data gathered during the static trial, further adapting the model to the patient spe-

cific anatomy18. This model is streamlined for gait analysis and employs standard gait analysis

approaches to compute the patient-specific gait pattern by means of a marker driven kinematic

model (Figure1c).

Method verification

Three healthy volunteer male subjects were invited for method verification experiments (S1: 19years,

79.4kg, 1.73m; S2:25years, 70.4kg, 1,69m; S3: 31years, 73.4kg, 1.82m). The scope of the verifica-

tion study was to evaluate the measurement chain and not to conduct a population study; hence

such a narrow patient collective was acquired. Three sub-studies for the method verification were

conducted which are divided in (i) dependency of hip flexion (hf) results on MoCap Analyst (ii)

dependency of hf results on marker-placement (iii) repeatability of hf results obtained. To answer

(i) three different examiners (A(experienced), B(experienced), C(not-experienced)) evaluated one

gait analysis conducted by a healthy subject (S1) ten times. To answer (ii) one healthy subject

conducted ten gait experiments (S1) and two experienced examiners (A,B) applied the marker set

in an alternating manner. For the robustness experiments the three healthy subjects (S1, S2, S3)

conducted ten gait experiments each, which were evaluated by one experienced examiner (A). Data

was processed with the same workflow as for the patient study. To evaluate the measurement chain

the standard error of mean (SEM) of the respective target parameter was computed according to

(2) with n samples and a sample standard deviation σ 19.

SEM(X) =σ√n

(1)

Subjective outcome measures

At all time points (t0,t1,t2) all patients answered the widely accepted clinical outcome scores the

Hip Osteoarthritis Outcome Score (HOOS)and the Harris Hip Score (HHS)20,21.

54

Patients and Methods Chapter IV Gait FF

input for...

input for...

acquisition ofvolunteer patients

- anthropometrics-anatomy

gait analysis- motion capture

(marker trajectories)- ground reaction forces

MM

scalinginput:

anthropometrics,MoCapData

output:segment lengths

& massesjoint positions

kinematic analysis:input:

scaling, gait analysisoutput:

gait pattern(joint angles)

a) workflow

80mm

b) marker set c) kinematic model

Figure 1: Gait analysis overview. a) patient/study workflow; b) marker set; c) kinematic and markerdriven model

55

Chapter IV Gait FF Results

Post-processing

Post-processing of data was done using MATLAB (MATLAB Release 2013a, The MathWorks, Inc.,

Natick, Massachusetts, United States.). Typical signals (TS) of hip flexion angles for every group

(CAS FF and CON) have been computed by means of dynamic time warping (dtw) and according

to Bender et al22. Dimensionless walking speed was computed according to Hof11 and according

to (2)

v =v√g · l0

(2)

The range of motion (ROM) depicts the active range of motion during of the patient during walking.

ROM symmetry is computed according to (3)

ROMsym =ROMop

ROMnoop(3)

Statistics

Statistical testing for significance (α ≤ 0.017) between CAS FF and CON for dimensionless pa-

rameters at different time points was done using two-way ANOVA in MATLAB. If data was not

normal distributed the non-parametric Kruskal-Wallis test for unequal sample sizes with α ≤ 0.017

was used23.

Results

Method verification

Figure 2 displays the verification study results (hip flexion, standard error of mean SEM). The

influence of MoCap Analyst should be noted. Marker Placement has the greatest influence on the

target parameters. The repeatability study shows that results are indeed robust, but care must

be taken when conducting experiments. A SEM of ±2.5◦ is an estimate of how accurate the hf

during walking can be computed, hence differences that are greater than 2 ·hfSEM = 5◦ are indeed

measurable. This is in agreement with literature data24.

Dimensionless walking speed

Figure 3 displays the dimensionless walking speed as calculated due to (2). The walking speed

increases significantly over the follow-up until reaching magnitude as measured in other studies25.

There is no significant difference between the intervention groups.

56

Results Chapter IV Gait FF

0.5

1

1.5

2

2.5

3right

Ana

lyst

A

SEM of hip flexion angled

evia

tion

indeg

ree

Ana

lyst

B

Ana

lyst

C

Place

men

tA

Place

men

tB

Subje

ct1

Subje

ct2

Subje

ct3

left

Figure 2: Method verification results

0.15

0.2

0.25

0.3

0.35

0.4

dim

ensi

onle

sssp

eed

dimensionless walking speed at follow-up points (operated side)

healthy speed (Stansfield et al)

patient speed (Stansfield et al.)

CAS

FFt0

CON

t0

CAS

FFt1

CON

t1

CAS

FFt2

CON

t2

Figure 3: dimensionless walking speed on the operated side including literature data

57

Chapter IV Gait FF Discussion

Kinematic parameters

Figure 4a displays the hip flexion range of motion (hfROM) at the different follow-up points and

divided in intervention groups. Figure4a also includes a benchmark ROM taken from literature26,27

that represent a ROM from young and healthy subjects and from fit and healthy older subjects.

The ROM increases over the follow-up period significantly, there were no differences between inter-

vention groups. We also tested hip abduction/adduction (ha) and hip rotation (hr). Ha also showed

a significant increase over the follow-up period between t0 and t1, while the increase between t1

and t2 was not-significant. Figure 4b shows the hfROM symmetry (ROMsym) as calculated by 3.

ROMsym increases over the follow-up period significantly, there were no differences between inter-

vention groups. The TS of hf as a function of 1 100% gait cycle as computed by dtw is displayed in

Figure 4c). Hf increases over the follow-up period and both groups reach normal kinematics at t2.

The CAS FF group seems to improve faster from t0 to t1 compared to the CON group; however

this difference is not significant. Most patients perform with normal kinematics at t1, but some

still improve measurable until t2, as indicated by the TS at t1 and t2.

gait cycle

-ext/

+fl

exan

gle

CAS FF

0 20 40 60 80 100

-20

-10

0

10

20t0t1t2norm

c) typical hip flexion angle (operated side)

gait cycle

-ext/

+fl

exan

gle

CON

0 20 40 60 80 100

-20

-10

0

10

20t0t1t2norm

0

10

20

30

40

50

60

RO

Min

deg

ree

a) hf ROM at follow-up

healthy (Schwartz et al.)fit and elderly(Winter et al.)

0

0.5

1

1.5

2

sym

met

ryin

dex

b) hf symmetry at follow-up

perfect symmetry

CAS

FFt0

CAS

FFt2

CAS

FFt1

CON

t0

CON

t1

CON

t2

CAS

FFt0

CAS

FFt2

CAS

FFt1

CON

t0

CON

t1

CON

t2

Figure 4: Kinematic parameters. a) hf ROM including benchmarks for healthy, young and fit, elderlysubjects; b) hf symmetry index including perfect symmetry c) hf at different follow up points for the CASFF and the conventional group; the gray shaded area indicates a healthy hf time series as gathered during

the method verification experiments.

58

Discussion Chapter IV Gait FF

Discussion

To the best of our knowledge this is the first study that investigated if gait performance can be

improved by CAS FF utilizing a combined workflow of experimental and computational methods.

In detail our hypotheses were (i) the dimensionless walking speed of the CAS FF group is im-

proved compared to conventional THR, (ii) the active range of motion is improved compared to

conventional THR and (iii) the walking pattern is more symmetrical in the CAS FF group when

compared to conventional THR group.

Our results indicate that the CAS FF patients perform with the same gait performance after THR

compared to the CON group. We realized a trend for improved hf for CAS FF at six month after

surgery but the differences did not reach statistical significance. While all parameters do increase

over the follow-up period for both of the groups, there were no significant differences between the

groups six month and one year after surgery. The results also show that the full walking ability of

patients is mostly restored six month after surgery regardless of the intervention technique. There

are several reasons why we could not accept our hypothesis that postoperatively CAS FF leads

to an improved walking performance when compared to conventional THR. First, six month after

surgery could be too late to measure early effect of CAS FF on walking ability. There might be an

effect on the target parameters before six month after the intervention, but there is no data available

at that time-point. We chose six months since our clinical experience is that patient compliance

seems to be good. Our drop-out rate supports this, only 2.56% patients eligible for pre-op analysis

did not return for the follow-up analysis. Second, a main goal of CAS FF is minimizing the risk

for impingement and dislocation by optimizing the implant position and orientation in order to

prevent impingement and decrease the risk for dislocation1. This is not necessarily related to

walking ability, as it is supported by our study. Walking ability is only restricted if impingement

occurs. The implant positions and orientations of the implant system that the surgeons achieved

during the different interventions are not critical for walking in terms of impingement. Third,

gait is not a critical motion. Impingement or dislocation is most likely to occur during critical

motion such as a squat or sit to stand maneuvers28,29. It is however unethical to invite patients

for experiments that might obviously result in failure of the implanted joint. Such research is

best carried out by facilitating a computational approach30–32. Patients may benefit the most

from CAS FF during critical motions but such research can only be carried out in silico, which

is a scope for further research. The reason why we chose walking is that it is the most common

activity of daily living (ADL) in THR patients33. Fourth, the effect may not be measurable or

the method is not sensible enough. Results gathered by means of gait analysis are subject to

variation which results on one hand from intra-individual differences in walking patterns. While

such intra-individual deviations can be countered by repeating the experiments several times and

choosing a representative experiment34, gait analysis is still subject to soft-tissue artifacts (STA)

of the attached markers during walking35. This might bias the results even if we tried to counter

that effect by defining the joint centers using a static trial, therefore canceling out the influence

of walking dynamics on STA. Previous studies have found a higher precision and fewer outliers in

terms of implant positioning for standard navigation algorithms for THR3,36–38. The navigated

Femur First technique takes this step even further by adjusting the acetabular cup relative to the

59

Chapter IV Gait FF Discussion

femoral components5. Investigating the long-term benefit of CAS FF is a scope for further research

and remains to be proven. While this is the first study conducted to research the walking ability

after CAS FF, several studies investigate the influence of different surgical techniques for THR.

These studies also find an overall improvement of gait performance after surgery, but no difference

in gait performance when operated with different surgical THR techniques (MIS vs. conventional,

CAS vs. conventional) has been found14,39. Strength of this study is the rather larger patient

number when using highly accurate 3D MoCap video based gait analysis. This method is rather

costly in terms of resources; so not many studies with such a large collective have been conducted so

far. There are other systems available that are not as laborious, with the disadvantage of being not

as accurate as 3D MoCap systems. We also conducted detailed and careful verification experiments

in order to determine the limits and capabilities of our system. Our patient collective was also

randomized as well as blinded, to the greatest extent. This fact cancels out placebo effects as well as

observer bias. Limiting is the fact that we did not include physical activity in particular. Patients

that are more active during their everyday life may recover faster than others. This may result in

a better walking ability. Since the participants were asked to walk the same way they normally do

we included this effect in the experiments. Also gait analysis is an objective measurement method,

therefore canceling out any psychological effects that might influence walking ability. By means of

clinical outcome scores (Hip Osteoarthritis Outcome Score, Harris Hip Score) we tried to counter

such effects. The scores also did not reveal any difference between the intervention groups at t0,

t1 and t2.

Conclusion

THR in general leads to an improved ability to walk six month after surgery. Patients undergoing

CAS FF showed a trend to an improved hip flexion angle compared to CON patients. The results

suggest that CAS FF may lead to a better performance. CAS FF delivers the same functional and

biomechanical outcome as CON, with a possible long-term benefit of reducing wear in the implant

system, which remains to be proven.

Acknowledgments

This work received funding from DePuy (IIS Reference Number: IIS2010013) International, Leeds,

UK as well as from Technologie und Wissenschaftsnetzwerk Ostbayern. We are also grateful for

support from Mr. Suess, Regensburg Center of Biomedical Engineering, for help with the dtw

algorithm, Mr. Zeman, Universitaetsklinikum Regensburg, Zentrum fuer klinische Studien, for

support in the statistical analysis and Mr. Waller, Universitaet Regensburg, British Studies for

the revision of this manuscript. We thank the University Medical Center Regensburg and the

RCBE for providing necessary infrastructure.

60

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